Vikram Anand

Predicting readmission risk with institution-specific prediction models

The ability to predict patient readmission risk is extremely valuable for hospitals, especially under the Hospital Readmission Reduction Program (HRRP) of the Center for Medicare and Medicaid Services (CMS) which went into effect starting October 1, 2012. There is a plethora of work in the literature that deals with developing readmission risk prediction models, but most of them do not have sufficient prediction accuracy to be deployed in a clinical setting, partly because different hospitals may have different characteristics in their patient populations. In this work we experimented with a generic framework for institution-specific readmission risk prediction, which takes patient data from a single institution and produces a statistical risk prediction model optimized for that particular institution and optionally condition specific. This provides great flexibility in model building, and is also able to provide institution-specific insights in its readmitted patient population. We showcase some initial results at three institutions for Heart Failure (HF), Acute Myocardial Infarction (AMI) and Pneumonia (PN) patients. The developed models yield better prediction accuracy than the ones present in the literature.

Benefit of computer-aided detection analysis for the detection of subsolid and solid lung nodules on thin- and thick-section CT

OBJECTIVE The objective of our study was to evaluate the impact of computer-aided detection (CAD) on the identification of subsolid and solid lung nodules on thin- and thick-section CT. MATERIALS AND METHODS For 46 chest CT examinations with ground-glass opacity (GGO) nodules, CAD marks computed using thin data were evaluated in two phases. First, four chest radiologists reviewed thin sections (reader(thin)) for nodules and subsequently CAD marks (reader(thin) + CAD(thin)). After 4 months, the same cases were reviewed on thick sections (reader(thick)) and subsequently with CAD marks (reader(thick) + CAD(thick)). Sensitivities were evaluated. Additionally, reader(thick) sensitivity with assessment of CAD marks on thin sections was estimated (reader(thick) + CAD(thin)). RESULTS For 155 nodules (mean, 5.5 mm; range, 4.0-27.5 mm)-74 solid nodules, 22 part-solid (part-solid nodules), and 59 GGO nodules-CAD stand-alone sensitivity was 80%, 95%, and 71%, respectively, with three false-positives on average (0-12) per CT study. Reader(thin) + CAD(thin) sensitivities were higher than reader(thin) for solid nodules (82% vs 57%, p < 0.001), part-solid nodules (97% vs 81%, p = 0.0027), and GGO nodules (82% vs 69%, p < 0.001) for all readers (p < 0.001). Respective sensitivities for reader(thick), reader(thick) + CAD(thick), reader(thick) + CAD(thin) were 40%, 58% (p < 0.001), and 77% (p < 0.001) for solid nodules; 72%, 73% (p = 0.322), and 94% (p < 0.001) for part-solid nodules; and 53%, 58% (p = 0.008), and 79% (p < 0.001) for GGO nodules. For reader(thin), false-positives increased from 0.64 per case to 0.90 with CAD(thin) (p < 0.001) but not for reader(thick); false-positive rates were 1.17, 1.19, and 1.26 per case for reader(thick), reader(thick) + CAD(thick), and reader(thick) + CAD(thin), respectively. CONCLUSION Detection of GGO nodules and solid nodules is significantly improved with CAD. When interpretation is performed on thick sections, the benefit is greater when CAD marks are reviewed on thin rather than thick sections.

Predicting Readmission Risk with Institution Specific Prediction Models

The ability to predict patient readmission risk is extremely valuable for hospitals, especially under the Hospital Readmission Reduction Program (HRRP) of the Center for Medicare and Medicaid Services (CMS) which went into effect starting October 1, 2012. There is a plethora of work in the literature that deals with developing readmission risk prediction models, but most of them do not have sufficient prediction accuracy to be deployed in a clinical setting, partly because different hospitals may have different characteristics in their patient populations. In this work we experimented with a generic framework for institution-specific readmission risk prediction, which takes patient data from a single institution and produces a statistical risk prediction model optimized for that particular institution and optionally condition specific. This provides great flexibility in model building, and is also able to provide institution-specific insights in its readmitted patient population. We showcase some initial results at three institutions for Heart Failure (HF), Acute Myocardial Infarction (AMI) and Pneumonia (PN) patients. The developed models yield better prediction accuracy than the ones present in the literature.

Improved Efficiency of CT Interpretation Using an Automated Lung Nodule Matching Program

OBJECTIVE The purpose of this study was to assess the impact of an automated program on improvement in lung nodule matching efficiency. MATERIALS AND METHODS Four thoracic radiologists independently reviewed two serial chest CT examinations from each of 57 patients. Each radiologist performed timed manual lung nodule matching. After 6 weeks, all radiologists independently repeated the timed matching portion using an automated nodule matching program. The time required for manual and automated matching was compared. The impact of nodule size and number on matching efficiency was determined. RESULTS An average of 325 (range, 244-413) noncalcified solid pulmonary nodules was identified. Nodule matching was significantly faster with the automated program irrespective of the interpreting radiologist (p < 0.0001 for each). The maximal time saved with automated matching was 11.4 minutes (mean, 2.3 ± 2.0 minutes). Matching was faster in 56 of 57 cases (98.2%) for three readers and in 46 of 57 cases (80.7%) for one reader. There were no differences among readers with respect to the mean time saved per matched nodule (p > 0.5). The automated program achieved 90%, 90%, 79%, and 92% accuracy for the four readers. The improvement in efficiency for a given patient using the automated technique was proportional to the number of matched nodules (p < 0.0001) and inversely proportional to nodule size (p < 0.05). CONCLUSION Use of the automated lung nodule matching program significantly improves diagnostic efficiency. The time saved is proportionate to the number of nodules identified and inversely proportional to nodule size. Adoption of such a program should expedite CT examination interpretation and improve report turnaround time.